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Software defect and complexity incidence relation analysis method based on machine learning

A software defect, machine learning technology, applied in software testing/debugging, instrumentation, electrical digital data processing, etc., can solve problems such as reducing software defects

Pending Publication Date: 2020-06-26
BEIJING INST OF COMP TECH & APPL
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is: how to design an objective and quantitative analysis method for software complexity and software defects, accurately locate the complexity measurement elements that affect software defects, and effectively reduce the number of software defects by reducing software complexity

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  • Software defect and complexity incidence relation analysis method based on machine learning
  • Software defect and complexity incidence relation analysis method based on machine learning
  • Software defect and complexity incidence relation analysis method based on machine learning

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Embodiment Construction

[0105] In order to make the purpose, content, and advantages of the present invention clearer, the specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0106] The overall design scheme of the present invention is as follows: First, analyze the characteristics of software in a specific field, and define the classification of software defects; for different programming languages, analyze its language characteristics, combine existing static analysis tools, define software complexity metrics, and design software complexity Then, based on the single-factor variance test and machine learning model, the relationship between the number of software defects and possible influencing factors is explored; in the single-variance test, the control variable method is used as the core to calculate the impact of different software defects. The impact of factors on the number of softwar...

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Abstract

The invention relates to a software defect and complexity incidence relation analysis method based on machine learning, and relates to the technical field of artificial intelligence and big data. Theinvention provides a the software defect and complexity association relationship analysis method based on machine learning, which is used for exploring the relationship between the number of differenttypes of software defects and multiple factors such as complexity and software types through single-factor variance test and Gaussian mixture model based on a large amount of software test data. In the gaussian mixture model, a complexity measurement result is subjected to cleaning and progressing. The relationship between the number of software defects and each complexity measurement is quantified. The invention discloses a Gaussian mixture model. A complexity measurement result is cleaned and processed; and quantitatively calculating an influence relationship between the number of the software defects and each complexity metric element, objectively analyzing Eemergence characteristics are analyzed between the software defects and the multiple software complexity metric elements on the basis of methods such as an AOV network, correlation analysis and the like, and thus further calculating key complexity factors influencing the software defects.

Description

technical field [0001] The invention relates to the technical fields of artificial intelligence and big data, in particular to a machine learning-based method for analyzing the correlation between software defects and complexity. Background technique [0002] In recent years, with the continuous development of new technologies such as artificial intelligence and big data, software evolution has become increasingly frequent, resulting in larger and more complex software systems. However, due to the exponential growth of software scale, ignoring software coding standards and software complexity control in order to complete the progress, the software complexity has risen sharply, which directly leads to the gradual increase of software defects, and the software quality has become more and more difficult to control. Therefore, it is particularly important to control software quality to study the relationship between software defects and software complexity, and to analyze the co...

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Application Information

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IPC IPC(8): G06F11/36
CPCG06F11/3604
Inventor 吴超柯文俊张在进高晨杨雨婷王坤龙
Owner BEIJING INST OF COMP TECH & APPL
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